Kelp Farm pH, Dissolved Oxygen and Control Sites

To translate kelp farm water-chemistry records easily, separate what a sensor measured from what the kelp may have caused. A pH reading describes a chemical condition at a particular place and time. Dissolved oxygen describes oxygen in the water. Neither number, by itself, proves that kelp cultivation has reversed ocean acidification, increased fish survival or removed a particular amount of atmospheric carbon dioxide.

Kelp photosynthesis can influence dissolved carbon and oxygen during daylight, while respiration and water exchange continue through the full day. A one-time morning reading may therefore disagree with an afternoon reading even when both instruments work correctly. Interpret the time series with tide, depth, temperature, salinity, calibration and a comparable reference site before making a farm-effect claim.

Quick interpretation: identify each variable and unit; specify the pH scale and dissolved-oxygen basis; align timestamps and sensor depths; inspect day-night patterns, tidal exchange and meteorology; compare a suitable control rather than only before versus after; check calibration drift and missing data; and distinguish observed differences from causally attributed ecosystem benefits.

A kelp farm is part of an exchanging water body

A seaweed growline sits in a moving coastal environment. Tides, currents, mixing, freshwater, sunlight, other organisms and chemical reactions continually alter the water that passes through the crop. Kelp is one contributor to these processes, not a sealed treatment vessel. That distinction matters whenever a reader encounters a graph labelled ‘water quality improvement’. The graph may contain valid measurements, yet attributing its movement to kelp requires additional evidence.

Consider a high-pH reading at a farm buoy. It could reflect local kelp photosynthesis, an incoming water mass with different chemistry, warming, instrument calibration or a combination. A nearby comparison station may help, but only if it samples relevant water at the right time and depth. The most honest introductory claim is that researchers are measuring whether a farm alters local conditions, not that every positive reading has already demonstrated a benefit.

What pH actually measures

pH is related logarithmically to hydrogen ion activity or concentration under a stated convention. Higher pH generally indicates lower acidity, but a difference of 0.1 pH unit is not a ten-percent difference in hydrogen ions. In a simplified comparison on the same scale and conditions, a 0.1-unit pH increase corresponds to a hydrogen-ion ratio of about 10 to the power of -0.1, or approximately 0.794, meaning about 20.6 percent lower hydrogen-ion activity.

That calculation does not mean the ocean is 20.6 percent safer for all organisms, because ecological effects depend on carbonate chemistry, duration, species and exposure. A difference between sensor values at two temperatures or on different pH scales can be more complicated. Preserve the measurement scale and method. Do not turn a precise chemical ratio into a claim about biological health without relevant organism-level data.

Why seawater has more than one pH scale

Marine chemistry publications may specify the total, free or seawater pH scale. The numerical readings are not always directly interchangeable because the conventions account differently for hydrogen-ion association with seawater components. A calibrated glass electrode and a spectrophotometric method may also be referenced differently. Before combining studies, find the scale, temperature and calibration information. A spreadsheet column called pH with none of these details is incomplete for high-precision carbonate work.

A useful translation retains notation such as pH on the total scale instead of shortening it to generic pH when a comparison depends on that convention. If a source lacks the scale, mark the ambiguity rather than guessing from location. Converting between scales requires appropriate salinity, temperature and thermodynamic constants, not a universal fixed offset. A reader should know whether a reported difference exceeds plausible measurement and conversion uncertainty.

Dissolved oxygen by concentration or saturation

Dissolved oxygen, often abbreviated DO, may be reported in milligrams of oxygen per litre, micromoles per kilogram, or percent saturation. These describe related but different quantities. Percent saturation compares the measured concentration with an estimated equilibrium concentration under stated temperature, salinity and pressure. Because oxygen solubility changes with those conditions, a constant concentration can correspond to different saturation percentages.

Suppose a report states 8 mg/L DO at a warm and a cold station. It cannot conclude that both are equally saturated without checking the equilibrium concentrations. Conversely, 100 percent saturation does not mean the same number of milligrams per litre at all temperatures. When translating a chart, keep the unit attached to each value and name the method used to calculate saturation. Do not confuse percentage saturation with percentage survival of organisms.

A checked DO saturation calculation

Imagine a synthetic seawater record with measured oxygen of 7.2 mg/L and a temperature-salinity-pressure-specific equilibrium concentration of 8.0 mg/L. The saturation percentage is 7.2 divided by 8.0 times 100, or 90 percent. If the equilibrium concentration were instead 7.5 mg/L under a second condition, the same 7.2 mg/L measurement would correspond to 96 percent saturation. The oxygen concentration itself did not change; the reference did.

The equilibrium values in this exercise are assumed for teaching, not borrowed as environmental thresholds. In real analysis, the relevant solubility calculation must be performed consistently. A 5 percent change in saturation can arise from temperature or pressure as well as biological oxygen production or consumption. A translation should state whether the reported saturation was measured by the instrument’s own compensation or calculated later from raw data.

Daylight photosynthesis and continuous respiration

Kelp uses light energy to fix inorganic carbon into organic matter and generally releases oxygen during oxygenic photosynthesis. Respiration consumes oxygen and releases carbon dioxide and continues outside daylight hours. Net observed water chemistry reflects those processes together with the activity of microbes, plankton and animals. Therefore, a pattern of increasing afternoon oxygen and declining nighttime oxygen can be consistent with metabolism, but the farm’s contribution must be separated from the wider ecosystem.

Some coastal waters show a strong daily cycle even without a kelp farm. The sun drives plankton photosynthesis, wind changes gas exchange, and currents carry different water masses. Describing an afternoon pH peak as ‘kelp effect’ without a control is a causal leap. A more careful wording is ‘pH and DO increased during daylight at the farm station; comparison with reference and physical data is required to identify the contribution of the cultivated kelp’.

Diel means a full roughly 24-hour cycle

Diel is a scientific term for processes varying across day and night. A study may sample every thirty minutes, every hour or only once per daylight shift. Those sampling rates produce very different visibility into a full cycle. A noon-only series cannot estimate nighttime minima. A weekly sample taken at different times each week can mimic a trend simply because the observations fall on different phases of the daily cycle.

A diel summary should include the time basis, local time zone, sunrise and sunset where relevant, and whether timestamps are in UTC or local standard time. If two sensors log every hour but one clock is displaced by thirty minutes, peaks will appear to occur at different times. Synchronize or retain the offset before comparing. Translating 06:00 UTC into local time without checking the offset can reverse whether a reading was taken at night or morning.

A simple day-night amplitude calculation

Suppose a fictional farm sensor records a daily pH minimum of 7.92 before sunrise and a maximum of 8.08 in late afternoon, on the same scale and with acceptable calibration. The observed daily amplitude is 8.08 minus 7.92, or 0.16 pH unit. That describes a range, not the net farm effect. If a control station over the same period ranges from 7.95 to 8.04, its amplitude is 0.09 unit.

The difference between amplitudes is 0.07 pH unit, but it is not automatically a causal kelp benefit. The stations could differ in depth, temperature, residence time or exposure to other biology. To interpret the result, compare the timing, paired observations and physical conditions. Avoid saying that the farm ‘raised ocean pH by 0.16’ when the figure was a within-farm range across a changing day.

The four-part marine carbonate picture

NOAA’s Ocean Acidification Program emphasizes four core variables: pH, partial pressure of carbon dioxide or pCO2, total alkalinity or TA, and dissolved inorganic carbon or DIC. DIC includes dissolved carbon dioxide and bicarbonate and carbonate forms. Total alkalinity describes the water’s ability to neutralize added acid under its chemical definition. pCO2 relates to the carbon dioxide chemical state. These measurements are linked by equilibrium chemistry under specified temperature, salinity and pressure.

One measured carbonate variable is rarely enough to reconstruct the full system. A calculation using two appropriate variables and established constants may estimate the other properties, but uncertainty and assumptions matter. Nutrients and acid-base constituents can complicate some waters. A table that gives pH, TA and DIC should clearly identify measured and calculated fields. Converting calculated pCO2 into a direct instrument measurement is an error.

What alkalinity does and does not say

Total alkalinity, often expressed as micromoles per kilogram, is not the same as pH. Two samples can have similar pH and different alkalinity, or similar alkalinity and different DIC and pH. A pH increase caused by photosynthetic uptake of carbon dioxide may occur without an equivalent increase in alkalinity. A translation should therefore resist terms such as ‘alkalinity improved’ when the record only shows pH.

An increase in TA also does not automatically prove kelp cultivation created carbonate buffering. Freshwater mixing, carbonate dissolution, sediment processes and other inputs can affect alkalinity. The relevant analysis asks how the water mass changed and which components were measured. For a public explanation, describe alkalinity as a chemical buffering property while preserving the measurement’s formal definition and units in the technical table.

pCO2 and what the direction of exchange means

Carbon dioxide partial pressure in seawater is usually expressed in microatmospheres or similar pressure units, while atmospheric carbon dioxide is often reported as a mixing ratio in parts per million. Those figures are related but not numerically identical in every setting. Their difference, along with exchange processes, helps describe potential air-sea carbon dioxide transfer. But potential flux and actual integrated exchange also depend on wind, transfer coefficients, surface conditions and timing.

A temporary reduction in water pCO2 inside a farm does not by itself establish durable atmospheric carbon removal. The change might be reversed by nighttime respiration or decomposition, or the water mass might leave the site. Calculating a short-term carbonate anomaly is useful research; assigning a climate credit requires a separate accounting framework with additionality, measurement, leakage and permanence. State the original study’s scope without stretching it.

Why a control station is more than a distant buoy

A reference station is intended to represent what conditions might have been without the local farm influence, while remaining sufficiently similar in hydrodynamic and environmental exposure. Placing it too close risks measuring farm-influenced water; placing it too far away risks comparing different water masses. No single distance is universally correct. Researchers use local circulation, depth, salinity patterns and site knowledge to design the control.

NOAA’s Point Judith kelp-farm dataset includes a farm and a control location, with autonomous and discrete observations during the December 2022 to May 2023 study period. Its metadata describe monitoring at around one metre depth and half-hour to hourly intervals. The methodological lesson is to document comparator design and sampling cadence, not to assume the study proved a particular benefit. The interpretation must follow the actual data and analysis.

Paired comparisons are stronger than separate averages

If farm and control readings occur at the same time and comparable depth, a paired difference can remove some shared background variability. Define delta pH as farm value minus control value on the same scale. A positive delta means the farm station recorded higher pH at that time, not automatically that kelp caused the difference. A paired series can be plotted by hour and tide stage to inspect persistence and timing.

Suppose paired pH values over four matched intervals give farm minus control differences of +0.03, +0.01, -0.02 and +0.04. Their simple mean is +0.015. This arithmetic is correct for the four valid pairs. It does not support reporting +0.015 as a precise seasonal effect because the small sample lacks coverage and uncertainty. If one control observation is missing, do not fabricate a pair to preserve a preferred average.

Difference in differences is not automatic causality

A before-after comparison with a control can ask whether changes at the farm differed from changes in background conditions. A simple difference-in-differences quantity is (farm after minus farm before) minus (control after minus control before). That framework can be informative, but its causal interpretation depends on assumptions such as comparable trends, absence of major confounding changes and well-defined treatment timing.

Take a teaching example: farm mean pH moves from 8.00 to 8.06, while reference mean moves from 7.99 to 8.02 using comparable methods. Farm change is +0.06, reference change +0.03, and difference in changes is +0.03. It would be inaccurate to call this proof that kelp added exactly 0.03 pH unit. Before drawing a causal conclusion, investigate sensor drift, circulation, season, biomass history and statistical uncertainty.

Tides and advection can dominate a time series

Tidal flow moves water through and around a cultivation area. An incoming water parcel may have different temperature, salinity, carbon chemistry and oxygen than the outgoing parcel. Water residence time controls how much local photosynthesis can change that parcel before it leaves. A daily pattern in pH may therefore reflect both daylight metabolism and tide timing, and those cycles can move in and out of phase across the season.

A useful data table aligns chemical observations with water level, current direction and velocity when available. The analysis may compare flood and ebb periods rather than averaging them together. If a station is near a river mouth, freshwater pulses may alter salinity and alkalinity substantially. A report with no hydrodynamic data can still describe observed variability honestly, but it should not promise a unique biological explanation for every peak and trough.

Depth and vertical structure matter

Water may be stratified by temperature or salinity, and photosynthesis near illuminated blades can differ from processes near the seabed. A sensor at one metre below the surface cannot necessarily describe oxygen or pH at three metres or in bottom water. State the position relative to the local surface and the bottom, the expected movement of the sensor with tide and its distance from the kelp canopy.

A vertical profile can reveal gradients that a surface logger misses. When interpreting such profiles, compare readings acquired close enough in time to avoid confusing time change with depth change. If a handheld probe is lowered over twenty minutes while currents shift, a seeming vertical pattern can also contain temporal variation. A translation should state sampling order and duration where relevant.

Temperature and salinity corrections

pH electrode response, chemical equilibria, oxygen solubility and many sensor compensations depend on temperature. Salinity affects conductivity-based estimates and carbonate-system calculations. Good field data record the associated water properties, not just pH or DO. A laboratory pH value measured after a water sample warms may differ from an in situ reading even when both are technically valid under their methods.

If a time series combines different instruments, harmonize their calibration conventions and identify where software applied temperature compensation. A DO percent-saturation field may be derived internally from an instrument’s temperature and salinity settings. If salinity was entered incorrectly, saturation can be biased even if the oxygen sensor itself is stable. Preserve raw measured fields when possible to support reprocessing and audit.

A high-frequency record needs quality flags

Half-hourly sensors generate many readings, but more values do not automatically create stronger evidence. Biofouling, battery loss, drift, servicing, out-of-water intervals and clock errors can contaminate a time series. A quality-control scheme should distinguish valid readings, provisional readings, instrument faults and missing periods. Removing obviously bad values may be appropriate under a documented rule, but deleting uncomfortable peaks merely because they weaken a hypothesis is not.

A graph shown to the public should indicate gaps and whether the displayed line includes interpolation or smoothing. A daily mean computed from twenty-four daytime readings is not a full-day mean. If a sensor missed all nighttime data, label the resulting statistic ‘daytime average’. A fixed percentage of acceptable samples can support comparability only if the missing data are not systematically related to the process under study.

Sensor calibration and drift

pH sensors may require checks with appropriate standards or seawater-relevant reference methods, while dissolved-oxygen probes need calibration and maintenance appropriate to their technology. The US Geological Survey’s continuous monitoring guidance addresses field cleaning, drift correction, documentation and quality review. Operators should follow instrument-specific procedures rather than treating one generic calibration routine as suitable for all devices.

A predeployment and postdeployment comparison can help identify sensor drift, but assuming drift is perfectly linear is itself an assumption. If a pH logger reads 8.04 against a suitable reference at the start and 8.10 against that same reference at the end, the 0.06-unit shift may warrant correction or data qualification depending on the validation method. It does not prove every midperiod reading was biased by a fixed 0.03. Preserve original and corrected versions with methods.

A checked sensor-offset example

Imagine a simple teaching exercise in which two pH sensors are checked against a shared reference solution under comparable conditions. Sensor A reads 8.05 and sensor B reads 8.00 while the appropriate reference value is 8.02. The instantaneous offsets are +0.03 for A and -0.02 for B. If both later measure the same seawater parcel and display 8.10 and 8.05, the raw difference is 0.05, matching the offset gap. That pattern could be purely instrumental.

Subtracting measured offsets would give corrected indicative readings of 8.07 for both sensors, but an official correction must follow the instrument’s calibration protocol, environmental range and uncertainty limits. The example shows why a small farm-control difference cannot be trusted without crosschecks. It is not a universal recipe for adjusting every pH series, and it should not replace proper recalibration or independent sample verification.

Spatial replication and independent controls

One farm sensor and one reference sensor can demonstrate local temporal patterns, but they give little information about spatial variability or instrument-specific bias. Additional positions along and outside a farm, repeated depths and independent water samples can help distinguish a coherent area-level signal from a sensor anomaly. The appropriate design depends on currents, farm dimensions, budget and the question being asked.

Do not treat thousands of half-hourly readings from one sensor as thousands of independent farm replicates. Time-series observations are autocorrelated: adjacent values tend to resemble one another. Statistical uncertainty must account for this structure and the number of truly independent stations or periods. A narrow confidence interval calculated by ignoring autocorrelation can make a weak effect look certain. Report the design as well as the number of rows.

Correlation between pH and oxygen is not proof of kelp causation

Photosynthesis can raise oxygen and alter dissolved inorganic carbon, so pH and DO may move together. But temperature, salinity, gas exchange, mixing and the metabolism of other organisms can also produce correlated variation. A scatterplot showing higher pH at higher oxygen does not establish which organism caused it. It also cannot identify net atmospheric carbon uptake without further evidence.

Before interpreting a correlation, inspect time lags, controls and seasonal regimes. If both variables peak in the afternoon at a farm and an unfarmed reference site, a shared daily driver may dominate the relationship. If the farm displays a different pattern under matched conditions, that difference merits examination, not an automatic claim of benefit. The strongest reports state alternative explanations and identify measurements that would discriminate among them.

Quantifying uncertainty without false precision

Sensor accuracy, calibration uncertainty, within-site variability, temporal variation and analytical methods all contribute to uncertainty. A reported difference of 0.01 pH unit may be important under precise laboratory methods but unreliable under poorly maintained field sensors. Do not round all values to the number of decimals supplied by a logger. Match reported precision to quality controls and research aims.

If two independent mean estimates are A ± uncertainty A and B ± uncertainty B, the uncertainty of their difference requires assumptions about covariance, sampling and the meaning of the stated uncertainties. Taking the smaller error bar of the two stations is not a valid shortcut. A field explanation can often be more responsible by showing observed distributions, matched differences and sensitivity to quality filtering rather than claiming a single definitive effect size.

What an ecosystem benefit claim would require

A farm might create a localized, time-dependent chemistry change that could be relevant to nearby calcifying organisms. Demonstrating a biological benefit, however, requires exposure and response evidence, not merely a pH sensor trace. The response depends on organisms, developmental stages, carbonate saturation, extreme events and other stresses. Even an observed improvement at the sensor location may not cover habitats down-current or deeper in the water column.

Studies of natural kelp forests have found that differences in pH and DO can be small and strongly shaped by hydrodynamics. That cautions against assuming an unlimited buffer. The scale and duration of effects are empirical questions. Translate ‘potential to modify local chemistry’ as potential, not as guaranteed rescue from acidification. Distinguish temporary local buffering from long-term regional chemistry changes.

Carbon dioxide removal is a separate accounting problem

Kelp uses dissolved inorganic carbon to grow, but the fate of its carbon is not automatically permanent. Harvested material may be consumed, processed or decomposed; detached material may remain nearshore or move elsewhere. To claim durable atmospheric removal, one needs evidence of air-sea exchange, carbon pathways, baseline conditions, additionality and permanence. A sensor showing temporary pH elevation does not contain those answers.

A rigorous report can still recognize valuable work without overstating it. Water-quality monitoring may provide better understanding of local variability and help farms manage other crops. It may reveal periods of stronger local photosynthetic influence. These are meaningful findings even when they do not support carbon-credit claims. The correct translation preserves the hierarchy between observation, process explanation, attribution and climate accounting.

A complete fictional daily-series exercise

Suppose a farm and control each have four synchronized pH values on the same scale, at comparable depth and after basic quality checks. The farm values are 7.98, 8.02, 8.07 and 8.03. The control values are 7.97, 8.01, 8.04 and 8.02. Paired differences are +0.01, +0.01, +0.03 and +0.01, averaging +0.015. The farm mean is 8.025 and the control mean is 8.010. These numbers are internally consistent.

Yet the exercise contains only four observations. It does not cover a full diel cycle, storms or multiple tides. It also assumes both devices are comparable and no values were rejected. A defensible summary says the farm station was higher by 0.015 pH unit on average over those four matched observations. It does not say that the kelp caused a precisely measured, enduring 0.015-unit increase across the bay.

A complete fictional DO time-series exercise

Now imagine three paired oxygen readings in mg/L. At the farm they are 6.8, 7.4 and 7.0; at the reference they are 6.7, 7.1 and 7.2. Differences are +0.1, +0.3 and -0.2 mg/L, averaging approximately +0.0667 mg/L. If a report rounds to +0.07 mg/L, it must keep the matched interval and sample size visible. The negative third pair is part of the record, not an inconvenience to remove.

Suppose the farm’s oxygen equilibrium concentration during one reading is 8.2 mg/L and the measured oxygen is 7.4 mg/L. Saturation is 7.4/8.2 × 100 ≈ 90.2 percent. A reference of 7.1 mg/L under different temperature or salinity cannot be compared by saturation without its own equilibrium value. The data permit a concentration comparison at those times, with environmental qualifications. They do not establish greater fish or shellfish survival.

When an intervention begins during a monitoring period

An experiment may have a predeployment baseline, a nursery outplanting date, biomass growth and final harvest. Those dates define changing treatment intensity, not one permanent on/off switch. A very young kelp crop has less biomass than a mature canopy, while removal at harvest abruptly changes the system. If treatment is represented only by a yes/no farm flag, analyses can obscure meaningful seasonality.

One useful study design documents actual biomass and crop coverage over time, then compares chemistry patterns before, during and after growth with matched references. But a season also brings changes in weather, other algae and water circulation. To argue causation, those factors must be considered. A statistical model cannot create information that was never measured. State which assumptions allow any estimated farm effect and which remain untested.

Field notes that rescue a suspicious graph

An odd pH spike can result from a sensor briefly emerging from the water, being cleaned, or recording another water parcel. The graph alone may not identify which. Field logs should give servicing times, unusual tides, physical disturbance, battery changes and independent spot samples. Plot those events alongside the chemistry before deciding whether a spike is a real phenomenon or a data-quality issue.

A simultaneous oxygen drop and salinity change may suggest a new water mass, but without current and temperature data it remains a hypothesis. Likewise a sudden flat line may signal sensor failure rather than exceptionally stable conditions. Preserve the raw data and add transparent quality flags. Deleting anomalies without recorded criteria risks erasing either important science or evidence of instrument problems.

How to translate charts without inventing significance

Headlines such as ‘kelp improved oxygen by 15 percent’ need a denominator and observation window. Does the figure describe concentration relative to reference, percent saturation difference, a model estimate or the fraction of readings above a threshold? Each has a different interpretation. If the original chart gives a mean difference, do not rewrite it as a maximum improvement. If it reports a confidence interval, retain it rather than selecting only the favorable boundary.

The use of axes also matters. A pH graph with a vertical scale from 7.9 to 8.1 will make changes appear visually large, which may be scientifically appropriate for a logarithmic variable. Do not call the plot deceptive merely because the axis is narrow; instead explain the scale and show numerical differences. A reader should be able to separate visual presentation from the size and uncertainty of the observed effect.

Independent transfer questions with answers

Question A: farm pH is 8.12 and matched control pH is 8.06. What is the observed difference? Answer: +0.06 pH unit on the same scale. Can you multiply 0.06 by 100 and call it six percent safer water? No. pH is logarithmic and biological effects require organism and chemistry evidence. Question B: DO is 6.0 mg/L and the appropriate equilibrium concentration is 7.5 mg/L. Saturation equals 80 percent; it is not an organism survival percentage.

Question C: four paired farm-control pH differences are +0.02, +0.04, -0.01 and +0.03. Their mean is +0.02. The negative pair must remain in the dataset. Question D: an hourly logger records from 08:00 through 18:00 only. Can its observations determine the day’s minimum oxygen? No, because nighttime data are absent. A useful answer distinguishes valid arithmetic from a claim that the sampling design cannot support.

Further transfer questions: time and carbonate chemistry

Question E: a logger writes 12:00 UTC and the study site is at a confirmed UTC minus four-hour offset on that date. The local wall time is 08:00, not 16:00. If daylight-saving rules change during deployment, the offset must be checked by date. Question F: pH rises, but TA and DIC were not measured. Can the complete carbonate system be calculated without assumptions? No. Additional measured variables or validated modeling assumptions are needed.

Question G: farm mean pH increases by 0.05 between two periods, while a comparable reference rises by 0.03. The descriptive difference in changes is +0.02. Explain why this alone does not prove a biological benefit. Answer: sensor quality, hydrodynamics, other photosynthetic organisms, baseline comparability, season and uncertainty may explain part or all of the difference, and no organism response was measured.

Frequently asked questions

Does higher afternoon pH prove kelp absorbed atmospheric CO2? No. It can be consistent with local photosynthesis but cannot by itself quantify atmospheric exchange or long-term fate. Is 100 percent DO saturation equal to 100 percent oxygen concentration? No; saturation is relative to equilibrium at specified conditions. Can pH values on different marine scales be compared directly? Not without appropriate definitions and transformations. Are thousands of readings from one logger thousands of independent samples? No.

Should a control station always be far away? Not necessarily; it must be sufficiently unaffected by the intervention yet environmentally comparable. Can a negative farm-control oxygen difference be reported? Yes, if the sensors and comparison are valid. What is the most reliable first repair to a chemistry spreadsheet? Add variable definition, unit, sensor, depth, timestamp and quality flag to each column. What is the strongest claim a single unverified peak supports? That a value was recorded, not that a beneficial ecological mechanism was demonstrated.

A readable conclusion should preserve the limits of evidence

A technically faithful explanation can still be clear: measurements show how pH and oxygen varied at a kelp farm and a reference location. Daylight metabolism, water movement and changing temperature are plausible contributors. The size and persistence of any farm-specific effect depend on quality-controlled paired observations and appropriate analysis. Benefits to organisms and long-term atmospheric carbon removal require their own evidence.

When translating these results into other languages, preserve the pH scale, units, symbols, confidence intervals, times, depths, and differences between measured and calculated quantities. The reader should leave understanding precisely what was observed and exactly what remains uncertain. Scientific honesty is not a weakness in a good explanation; it is what allows the next investigator to use the data correctly.